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Al Developer Jobs (NOW HIRING)

Noblis

Al Engineer

Bethesda, MD · On-site

Ensuring Al solutions incorporate security controls and responsible Al practices appropriate to the environment. * Collaborate with AI/ML Modeler Engineers, Data Scientists, and Data Engineers to ...

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Al Developer information

How does an AI Developer typically collaborate with data scientists and software engineers on projects?

AI Developers frequently work in cross-functional teams, collaborating closely with data scientists to understand data requirements and model objectives, and with software engineers to integrate AI solutions into larger applications. Effective communication is essential, as AI Developers often translate complex technical concepts into actionable tasks for team members. Regular meetings, code reviews, and shared development environments are common practices to ensure alignment and smooth project progress. This collaborative structure helps deliver robust and scalable AI-driven products.

Which 3 jobs will survive AI?

For AI developers, roles that require complex problem-solving, creativity, and emotional intelligence are more likely to persist, such as AI ethics specialists, AI trainers and data annotators, and cybersecurity experts. These jobs involve tasks that are difficult to automate and often require human judgment, domain expertise, and adaptability. Continuous learning and certification in relevant tools like machine learning frameworks can also enhance job security.

What is the difference between Al Developer vs Data Scientist?

AspectAl DeveloperData Scientist
Required CredentialsBachelor's in CS, AI, or related fields; programming skills in Python, TensorFlowBachelor's or higher in Statistics, CS, or related; strong analytical skills
Work EnvironmentDevelops AI models, algorithms, and applications; often in tech or AI-focused companiesAnalyzes data, builds predictive models, reports insights; in tech, finance, healthcare sectors
Employer & Industry UsageTech companies, AI startups, R&D labsTech firms, finance, healthcare, research institutions

While both roles involve working with data and programming, Al Developers focus on creating AI algorithms and applications, whereas Data Scientists analyze data to extract insights and build models. The roles often overlap but differ in primary objectives and skill emphasis.

What are AI Developers?

AI Developers are professionals who design, build, and implement artificial intelligence solutions, such as machine learning models, neural networks, and natural language processing systems. They work with large datasets, programming languages, and AI frameworks to create intelligent software applications that can automate tasks, analyze data, and solve complex problems. AI Developers often collaborate with data scientists, engineers, and business stakeholders to integrate AI capabilities into various products and services.

What jobs are there in AI development?

Jobs in AI development include roles such as AI Software Engineer, Machine Learning Engineer, Data Scientist, Deep Learning Engineer, and AI Research Scientist. These positions typically require skills in programming languages like Python, knowledge of machine learning frameworks, and understanding of algorithms and data structures.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI researcher, machine learning director, or AI executive, often requiring advanced skills in programming, data analysis, and deep learning. These roles usually involve leadership responsibilities, strategic planning, and extensive experience, and they may be found in large tech companies or specialized AI firms.

What are the key skills and qualifications needed to thrive as an AI Developer, and why are they important?

To thrive as an AI Developer, you need strong programming skills (especially in Python), a solid background in mathematics and statistics, and typically a degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), cloud platforms, and version control systems is essential, and certifications in AI or data science can be advantageous. Creativity, analytical thinking, and effective communication help you design innovative solutions and collaborate with multidisciplinary teams. These skills and qualities are crucial for developing robust AI models that solve real-world problems efficiently and responsibly.

How much do AI developers get paid?

AI developers typically earn between $80,000 and $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in machine learning, deep learning, or specific tools like TensorFlow or PyTorch tend to command higher salaries.
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Al Developer jobs near you

Hiring: Al Developer / Agentic Al Engineer

Realtech Services

Charlotte, NC • On-site

Contractor

Re-posted 4 days ago

Job description


Position: Al Developer / Agentic Al Engineer- 12 Positions

Location: Charlotte, NC

Interview Process: F2F Required at Charlotte, NC

Our challenge:

  • Candidate will be building an agentic Al platform to transform commercial banking customer service. The Al developer will design, build, and operate LLM-powered agents that interpret inbound servicing requests (e.g., email / case intake), retrieve grounded knowledge, and execute approved workflows through secure too/API integrations - with enterprise-grade controls, observability, and human-in-the-loop patterns.
  • This role sits within a cross-functional team with Product, Operations, Technology, and Risk partners and focuses on delivering production-ready agentic Al capabilities for regulated financial services Responsibilities.

The Role

Responsibilities:

Agentic Al Solution Development

  • Build and enhance LLM/agent orchestration (Planner/supervisor patterns, tool-using agents, routing, guardrails).
  • Implement intent classification information extraction validation and decision logic for servicing workflows
  • Developed tool calling integrations to downstream systems (CRM, workflow engine, core banking services, case management)
  • Implement human-in-the-loop workflows (review, approval, escalation, override) based on confidence/risk thresholds.

Knowledge and grounding (RAG)

  • Design and implement retrieval-augmented generation (RAG) for policy procedure grounding and resolution guidance.
  • Build knowledge ingestion pipelines with refresh/versioning.
  • Improve answer quality via chunking strategies, embeddings re ranking and context management.

Quality, Safety and Evaluation

  • Define and run evaluation frameworks: golden datasets, scenario tests, regression tests, and automated scoring.
  • Reduce hallucinations and risk by implementing prompt policies, constraints, structured outputs, and verification steps.
  • Partner with risk slash compliance to ensure traceability, audit logs, explain ability requirements are met.

Production Readiness and Operations

  • Implement observability for agents (latency, cost, tool failures, drift, quality signals, escalation rates).
  • Support CI/CD for agent prompts and configurations (versioning, approvals, rollback).
  • Collaborate with platform and security teams on secrets management, access controls, PIl protections, and safe deployments.

Requirements:

  • 4+ years of software engineering experience or equivalent with strong CS fundamentals
  • Hands-on experience building with LLMs and modern Al app stack (agents, RAG, tool/function calling).
  • Strong proficiency in Python and building back-end services/APls.
  • Experience with at least one: LangChain/ LangGraph, Llamalndex, Semantic Kernel or equivalent frameworks.
  • Experience with vector databases and search (e.g., Pinecone, Weaviate, Milvus, OpenSearch/Elastic, )
  • Experience deploying services in cloud environments (AWS/Azure/GP) with basic DevOps practices
  • Strong understanding of security and privacy principles (PIl handling, least privilege, audit logging).

Preferred:

but not required:

  • Experience in financial services or other regulated domains (risk controls, compliance audit readiness)
  • Experience integrating with enterprise workflows (e.g., ServiceNow, Custom workflow engines,
  • BPM/RPA)
  • Familiarity with model evaluation approaches (LLM-as-judge, rubric scoring, retrieval evals, offline/online testing)
  • Experience with messaging/eventing (Kafka/SQS), email ingestion pipelines, and document processing
  • Exposure to MRM concerns and governance (model cards, risk assessments, validation processes)